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Penalized full likelihood approach to variable selection for Cox’s regression model under nested case–control sampling

Author

Listed:
  • Jie-Huei Wang

    (National Health Research Institutes
    Academia Sinica)

  • Chun-Hao Pan

    (Academia Sinica)

  • I-Shou Chang

    (National Health Research Institutes
    National Health Research Institutes)

  • Chao Agnes Hsiung

    (National Health Research Institutes)

Abstract

Assuming Cox’s regression model, we consider penalized full likelihood approach to conduct variable selection under nested case–control (NCC) sampling. Penalized non-parametric maximum likelihood estimates (PNPMLEs) are characterized by self-consistency equations derived from score functions. A cross-validation method based on profile likelihood is used to choose the tuning parameter within a family of penalty functions. Simulation studies indicate that the numerical performance of (P)NPMLE is better than weighted partial likelihood in estimating the log-relative risk and in identifying the covariates and the model, under NCC sampling. LASSO performs best when cohort size is small; SCAD performs best when cohort size is large and may eventually perform as well as the oracle estimator. Using the SCAD penalty, we establish the consistency, asymptotic normality, and oracle properties of the PNPMLE, as well as the sparsity property of the penalty. We also propose a consistent estimate of the asymptotic variance using observed profile likelihood. Our method is illustrated to analyze the diagnosis of liver cancer among those in a type 2 diabetic mellitus dataset who were treated with thiazolidinediones in Taiwan.

Suggested Citation

  • Jie-Huei Wang & Chun-Hao Pan & I-Shou Chang & Chao Agnes Hsiung, 2020. "Penalized full likelihood approach to variable selection for Cox’s regression model under nested case–control sampling," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(2), pages 292-314, April.
  • Handle: RePEc:spr:lifeda:v:26:y:2020:i:2:d:10.1007_s10985-019-09475-z
    DOI: 10.1007/s10985-019-09475-z
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    References listed on IDEAS

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    1. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    2. Thomas H. Scheike & Torben Martinussen, 2004. "Maximum Likelihood Estimation for Cox's Regression Model Under Case–Cohort Sampling," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(2), pages 283-293, June.
    3. Kani Chen, 2001. "Generalized case–cohort sampling," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(4), pages 791-809.
    4. Zhao, Sihai Dave & Li, Yi, 2012. "Principled sure independence screening for Cox models with ultra-high-dimensional covariates," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 397-411.
    5. Ørnulf Borgan & Ying Zhang, 2015. "Using cumulative sums of martingale residuals for model checking in nested case‐control studies," Biometrics, The International Biometric Society, vol. 71(3), pages 696-703, September.
    6. Ai Ni & Jianwen Cai & Donglin Zeng, 2016. "Variable selection for case-cohort studies with failure time outcome," Biometrika, Biometrika Trust, vol. 103(3), pages 547-562.
    7. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
    8. Sihai Dave Zhao & Yi Li, 2014. "Score test variable screening," Biometrics, The International Biometric Society, vol. 70(4), pages 862-871, December.
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